Production LLM applications often break silently: hallucinations, high latency, uncontrolled token cost growth. The average cost of an hour of blind debugging depends on scope, and a week of uncontrolled hallucinations can cost reputation and budget. AI observability encompasses LLM monitoring, call tracing, and prompt quality evaluation to prevent these problems. Without observability, you debug blindly—guessing which prompt caused degradation or which model burned the budget. One of our clients lost 3,000 rubles in a month due to an abnormal token surge that went untracked. We solve this problem: we set up dedicated observability stacks based on LangSmith, Langfuse, or Helicone, adapted to your infrastructure. Our engineers have MLOps certifications and experience with LLM applications in production.
Typical Problems We Solve
- Lost traces. When a LangChain call chain breaks, you can't see where the error occurred. LangSmith documentation states that each call preserves the full context. According to the documentation, each call preserves the full stack trace.
- Unknown cost. Without model and token tracking, the budget disappears into thin air. Both platforms calculate cost on the fly based on model and token count.
- Quality degradation. A new prompt version produces 20% irrelevant answers—you find out a week later. We set up automatic evaluations (relevance, faithfulness) with alerts when metrics drop.
Why Choose Langfuse for Self-Hosted?
For companies with GDPR, HIPAA, or corporate data residency requirements, Langfuse is the only adequate solution. It is fully open-source and deployed via Docker Compose on your servers. For one client with European regulations, we deployed Langfuse in a closed environment, integrated it with their MLflow, and configured custom metrics: relevance on a 0-1 scale and faithfulness. Result: debugging time reduced by 40%, token costs by 15% due to detection of suboptimal prompts. Compared to LangSmith, self-hosted Langfuse is 2-3 times cheaper for high request volumes.
How to Integrate Observability into an Existing ML Pipeline?
The process consists of five stages:
- Analysis — audit of current LLM calls, identification of trace points, agreement on SLAs.
- Design — selection of tool (LangSmith for SaaS, Langfuse for self-hosted), data schema setup.
- Implementation — installation of agents, integration of
@observedecorators or interceptors, custom scoring. - Testing — measurement of p99 latency, verification of alerts, comparison with baseline.
- Deployment and documentation — rollout, team instructions, training.
What Is Included in the Work
| Stage | Duration | Result |
|---|---|---|
| Analysis | 1-2 days | Report on current monitoring points |
| Design | 1 day | Integration scheme, tool selection |
| Implementation | 2-4 days | Working pipeline with traces and evaluations |
| Testing | 1 day | Confirmed reduction in p99 latency by 30% and errors |
| Documentation | 1 day | Instructions, dashboards, alerts |
Deliverables include: detailed documentation, access credentials for all tools, team training session, and 1 month of post-launch support. This ensures your team can independently manage the observability stack.
Timeline: from 3 to 10 days depending on complexity. Cost is calculated individually after an audit of your project.
Platform Comparison
| Parameter | LangSmith | Langfuse | Helicone |
|---|---|---|---|
| Self-hosted | No (SaaS) | Yes (Open Source) | No |
| Integration with LangChain | Native | Via callback | REST API |
| Free tier | Limited | Fully free (self-hosted) | 100k requests/month |
| Custom metrics | Via datasets | Built-in scoring | Via API |
| Data residency | No | Full control | No |
Langfuse is preferred for data residency requirements, Helicone for quick integration with any API, LangSmith for deep LangChain integration.
Common Implementation Mistakes
- Trying to implement observability after deployment — it's an order of magnitude harder than building it in from the first commit.
- Ignoring quality evaluation in code: without
score_current_trace, you won't see metric drops in real time. - Setting too many alerts — the team stops responding. Optimal: 3-4 key metrics (p99 latency, cost per query, error rate, relevance score).
Quick Start Code (LangSmith)
pip install langchain langsmith export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY=ls__xxx export LANGCHAIN_PROJECT=my-llm-app from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate llm = ChatOpenAI(model="gpt-4o") prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant"), ("user", "{question}") ]) chain = prompt | llm result = chain.invoke({"question": "What is RAG?"}) A trace with full information will automatically appear in LangSmith.
Langfuse Self-Hosted (Docker)
docker compose up -d # from langfuse/langfuse repository from langfuse import Langfuse from langfuse.decorators import observe, langfuse_context langfuse = Langfuse( public_key="pk-xxx", secret_key="sk-xxx", host="http://localhost:3000" ) @observe() def handle_query(query: str) -> str: context = retrieve(query) response = generate(query, context) langfuse_context.score_current_trace( name="relevance", value=0.95, comment="Adequate context" ) return response Both platforms automatically log cost and latency. We set up alerts for budget overruns or abnormal latency spikes.
Example Cost Savings Calculation
After implementing observability, one client achieved significant savings. Debugging time decreased by 40%, equivalent to freeing up 1-2 person-hours daily.
Our certified engineers with MLOps experience guarantee that you will get a transparent, manageable LLM product. We will assess your project in one day—just contact us. Get a consultation on observability for your LLM application—we will evaluate the current state and propose an optimal solution. Receive a detailed observability implementation plan tailored to your requirements.